The accurate solution to many simulation equations requires the number crunching power of modern supercomputers. To simulate air flow in a complex duct or the deformation of an automobile chassis under a mechanical load, an engineer typically must first decompose the object of interest into thousands of small six faced volumes (hexahedrons) in the case of the fluid flow analysis, or triangles and four sided plane shapes (quadrilaterals) when dealing with the modeling of sheet metal. This decomposition is known as a computational grid, and the set of computer instructions that represent the simulation program constitute the heart of numerical simulation in engineering. Engineers can program a set of computer instructions necessary to solve the equations of physics in simple domains such as triangles and hexahedrons. Supercomputers, on the other hand, are capable of working out the solution of equations for thousands of tiny triangles and hexahedrons at a fast pace. The result of the computer program operating on a decomposed object is the reproduction of the desired physical phenomenon for the complete object.
Each new engineering problem requires the creation of a new grid. The simulation software always stays the same, but the grid changes from problem to problem. For instance, for an automotive firm the successful application of the latest numerical simulation techniques to products requires that grids be built for all the possible variations of the final product and numerical simulation be performed on each grid. To this end, many engineers are employed to build numerical grids from either blueprints or computer-aided design (CAD) definitions.
As CAD has revolutionized the design process by making it faster, more accurate, and cheaper, all major product manufacturing industries world wide have almost all the parts of the products that they manufacture represented in CAD systems. When an engineer wishes to view a specific part, the part can be recalled on a workstation and viewed in a three dimensional space represented on the screen of the workstation.
The current process for designing and testing complex objects begins with the development of the CAD definition of the parts of the object. Once the parts have been modeled, testing begins by using a supercomputer to simulate the forces that will act upon the object when it is in use. To perform this numerical analysis, a computational grid of the object must be built mostly by hand. Once a grid has been superimposed, the simulation is run and engineers then make design changes to the CAD definition of the object based upon the simulation results. The simulation may then be repeated before finally building a prototype or model of the object. Once a physical model has been built, physical testing of that object can be performed, and any final changes may be made before final tooling to put the object into production.
While grid generation is an essential step in simulating an object in use, it is also a very tedious and labor intensive step. Having a blueprint or even an advanced mathematical description of the shape of the object represents only a first step in the grid generation process. The core of the grid generation task is performed by an engineer working in front of an engineering workstation and creating the final grid by hand. Commercial grid generation software is typically used to produce the grid from CAD information, but although the software generally offers a great variety of features and utilities that considerably facilitate grid generation, the task is still labor intensive and supercomputing has had no role in it to date.
As an example of the intensive aspect of grid generation, to create a computational grid for the analysis of air flow under the hood of an automobile may take six months and cost well over $100,000. To create the computational grid of an automotive engine combustion chamber may take in excess of two weeks and cost over $10,000.
Therefore, there is a need for a volume grid generator which is capable of automatically producing grids for volumes of very complex shapes in a small amount of time.